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GapAnalysis (version 1.0.2)

GRSex: Geographical representativeness score ex situ

Description

The GRSex process provides a geographic measurement of the proportion of a species<U+2019> range that can be considered to be conserved in ex situ repositories. The GRSex uses buffers (default 50 km radius) created around each G coordinate point to estimate geographic areas already well collected within the distribution models of each taxon, and then calculates the proportion of the distribution model covered by these buffers.

Usage

GRSex(
  Species_list,
  Occurrence_data,
  Raster_list,
  Buffer_distance = 50000,
  Gap_Map = FALSE
)

Arguments

Species_list

A vector of characters with the species names to calculate the GRSex metrics.

Occurrence_data

A data frame object with the species name, geographical coordinates, and type of records (G or H) for a given species

Raster_list

A list of rasters representing the species distribution models for the species list provided in Species_list. The order of rasters in this list must match the same order as Species_list.

Buffer_distance

Geographical distance used to create circular buffers around germplasm. Default: 50000 (50 km) around germplasm accessions (CA50)

Gap_Map

logical, if TRUE the function will calculate gap maps for each species analyzed and will return a list with two slots GRSex and gap_maps. If any value is provided, the function will assume that Gap_Map = TRUE

Value

This function returns a data frame with two columns:

species Species name GRSex
GRSex value calculated species Species name

References

Ramirez-Villegas et al. (2010) PLOS ONE, 5(10), e13497. doi: 10.1371/journal.pone.0013497 Khoury et al. (2019) Ecological Indicators 98:420-429. doi: 10.1016/j.ecolind.2018.11.016

Examples

Run this code
# NOT RUN {
##Obtaining occurrences from example
data(CucurbitaData)
Cucurbita_splist <- unique(CucurbitaData$species)
## Obtaining rasterList object. ##
data(CucurbitaRasters)
CucurbitaRasters <- raster::unstack(CucurbitaRasters)
#Running GRSex
GRSex_df <- GRSex(Species_list = Cucurbita_splist,
                    Occurrence_data = CucurbitaData,
                    Raster_list = CucurbitaRasters,
                    Buffer_distance = 50000,
                    Gap_Map = TRUE)

# }

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